Retail shrink mostly comes from customers stealing, cashier sweet-hearting, and employee theft. Item substitution fraud occurring at Self-Service Checkout (SSCO) and Point-Of-Sale (POS) terminals is one of the key shrink issues. Perpetrators commit the fraud by checking out an expensive item (e.g., a bottle of bourbon) as a cheap produce item (e.g., banana). For example, rather than scanning a barcode of an item, the item is placed on a scale, an item type is keyed in or otherwise selected such as bananas, and the item is priced at checkout by weight rather than by the product barcode.
Previous product identification approaches have relied on item databases including reference product image(s) and feature data. These solutions require extensive computational resources and large databases, which results in more expensive SSCO and POS terminal hardware and slower system performance. Some efforts have been made in texture-based produce classification techniques to verify produce items, but these efforts have failed to provide reliable results. Additionally, specular reflection (e.g., glare) is an issue in all of these image-based efforts as specular reflection has confounded reliable results due at least to specular reflection obscuring features of presented items that are utilized by their respective algorithms.